SOTAVerified

Meta-Learning

Meta-learning is a methodology considered with "learning to learn" machine learning algorithms.

( Image credit: Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks )

Papers

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Showing 241–250 of 3569 papers

TitleStatusHype
Coordinated Control of Deformation and Flight for Morphing Aircraft via Meta-Learning and Coupled State-Dependent Riccati Equations—0
Meta-learning-based percussion transcription and tala identification from low-resource audio—0
Optimizing Value of Learning in Task-Oriented Federated Meta-Learning Systems—0
AMM: Adaptive Modularized Reinforcement Model for Multi-city Traffic Signal Control—0
MetaNeRV: Meta Neural Representations for Videos with Spatial-Temporal Guidance—0
Adaptive Meta-learning-based Adversarial Training for Robust Automatic Modulation Classification—0
Social Processes: Probabilistic Meta-learning for Adaptive Multiparty Interaction ForecastingCode0
Online Meta-Learning Channel Autoencoder for Dynamic End-to-end Physical Layer Optimization—0
Meta-Learning Hyperparameters for Parameter Efficient Fine-Tuning—0
LoRA Recycle: Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAsCode1
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All datasetsML10MT50ML45OMNIGLOT - 1-Shot, 20-way

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1MZ+ReconMeta-train success rate97.8—Unverified
2MZMeta-train success rate97.6—Unverified
3MAMLMeta-test success rate36—Unverified
4RL^2Meta-test success rate10—Unverified
5DnCMeta-test success rate5.4—Unverified
6PEARLMeta-test success rate0—Unverified
#ModelMetricClaimedVerifiedStatus
1SoftModuleAverage Success Rate60—Unverified
2Multi-task multi-head SACAverage Success Rate35.85—Unverified
3DisCorAverage Success Rate26—Unverified
4NDPAverage Success Rate11—Unverified
#ModelMetricClaimedVerifiedStatus
1MZ+ReconMeta-test success rate (zero-shot)18.5—Unverified
2MZMeta-test success rate (zero-shot)17.7—Unverified
#ModelMetricClaimedVerifiedStatus
1Metadrop% Test Accuracy95.75—Unverified